


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     :					List[str]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					Dict      =       {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     for attribute in key.split('''.'''							):
          snake_case											=							getattr(UpperCamelCase_				,UpperCamelCase_							)

     if weight_type is not None:
          snake_case											=							getattr(UpperCamelCase_				,UpperCamelCase_							).shape
     else:
          snake_case											=							hf_pointer.shape

     assert hf_shape == value.shape, (
         F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
         F''' {value.shape} for {full_name}'''
     )

     if weight_type == "weight":
          snake_case											=							value
     elif weight_type == "weight_g":
          snake_case											=							value
     elif weight_type == "weight_v":
          snake_case											=							value
     elif weight_type == "bias":
          snake_case											=							value
     else:
          snake_case											=							value

     logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							[]
     snake_case											=							fairseq_model.state_dict()

     snake_case											=							hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          snake_case											=							False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,hf_model.config.feat_extract_norm == '''group'''				,)
               snake_case											=							True
          else:
               for key, mapped_key in MAPPING.items():
                    snake_case											=							'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                    if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
                         snake_case											=							True
                         if "*" in mapped_key:
                              snake_case											=							name.split(UpperCamelCase_							)[0].split('''.'''							)[-2]
                              snake_case											=							mapped_key.replace('''*'''				,UpperCamelCase_							)
                         if "weight_g" in name:
                              snake_case											=							'''weight_g'''
                         elif "weight_v" in name:
                              snake_case											=							'''weight_v'''
                         elif "weight" in name:
                              snake_case											=							'''weight'''
                         elif "bias" in name:
                              snake_case											=							'''bias'''
                         else:
                              snake_case											=							None
                         set_recursively(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							)
                    continue
          if not is_used:
               unused_weights.append(UpperCamelCase_							)

     logger.warning(F'''Unused weights: {unused_weights}'''							)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							full_name.split('''conv_layers.'''							)[-1]
     snake_case											=							name.split('''.'''							)
     snake_case											=							int(items[0]							)
     snake_case											=							int(items[1]							)

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
               )
               snake_case											=							value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
               )
               snake_case											=							value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                   " found."
               )
               snake_case											=							value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
               )
               snake_case											=							value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
     else:
          unused_weights.append(UpperCamelCase_							)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							SEWConfig()
     if is_finetuned:
          snake_case											=							model.wav_encoder.wav_model.cfg
     else:
          snake_case											=							model.cfg

     snake_case											=							fs_config.conv_bias
     snake_case											=							eval(fs_config.conv_feature_layers							)
     snake_case											=							[x[0] for x in conv_layers]
     snake_case											=							[x[1] for x in conv_layers]
     snake_case											=							[x[2] for x in conv_layers]
     snake_case											=							'''gelu'''
     snake_case											=							'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
     snake_case											=							0.0
     snake_case											=							fs_config.activation_fn.name
     snake_case											=							fs_config.encoder_embed_dim
     snake_case											=							0.02
     snake_case											=							fs_config.encoder_ffn_embed_dim
     snake_case											=							1e-5
     snake_case											=							fs_config.encoder_layerdrop
     snake_case											=							fs_config.encoder_attention_heads
     snake_case											=							fs_config.conv_pos_groups
     snake_case											=							fs_config.conv_pos
     snake_case											=							len(UpperCamelCase_							)
     snake_case											=							fs_config.encoder_layers
     snake_case											=							fs_config.squeeze_factor

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          snake_case											=							model.cfg
          snake_case											=							fs_config.final_dropout
          snake_case											=							fs_config.layerdrop
     snake_case											=							fs_config.activation_dropout
     snake_case											=							fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     snake_case											=							fs_config.attention_dropout
     snake_case											=							fs_config.dropout_input
     snake_case											=							fs_config.dropout
     snake_case											=							fs_config.mask_channel_length
     snake_case											=							fs_config.mask_channel_prob
     snake_case											=							fs_config.mask_length
     snake_case											=							fs_config.mask_prob

     snake_case											=							'''Wav2Vec2FeatureExtractor'''
     snake_case											=							'''Wav2Vec2CTCTokenizer'''

     return config
@torch.no_grad()
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_=None				,UpperCamelCase_=None				,UpperCamelCase_=True							):



     """simple docstring"""






     if is_finetuned:
          snake_case					,    snake_case					,    snake_case											=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]				,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
     else:
          snake_case					,    snake_case					,    snake_case											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

     if config_path is not None:
          snake_case											=							SEWConfig.from_pretrained(UpperCamelCase_							)
     else:
          snake_case											=							convert_config(model[0]				,UpperCamelCase_							)
     snake_case											=							model[0].eval()

     snake_case											=							True if config.feat_extract_norm == '''layer''' else False
     snake_case											=							WavaVecaFeatureExtractor(
         feature_size=1				,sampling_rate=1_60_00				,padding_value=0				,do_normalize=UpperCamelCase_				,return_attention_mask=UpperCamelCase_				,)

     if is_finetuned:
          if dict_path:
               snake_case											=							Dictionary.load(UpperCamelCase_							)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               snake_case											=							target_dict.pad_index
               snake_case											=							target_dict.bos_index
               snake_case											=							target_dict.pad_index
               snake_case											=							target_dict.bos_index
               snake_case											=							target_dict.eos_index
               snake_case											=							len(target_dict.symbols							)
               snake_case											=							os.path.join(UpperCamelCase_				,'''vocab.json'''							)
               if not os.path.isdir(UpperCamelCase_							):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_							)							)
                    return
               os.makedirs(UpperCamelCase_				,exist_ok=UpperCamelCase_							)
               with open(UpperCamelCase_				,'''w'''				,encoding='''utf-8'''							) as vocab_handle:
                    json.dump(target_dict.indices				,UpperCamelCase_							)
               snake_case											=							WavaVecaCTCTokenizer(
                   UpperCamelCase_				,unk_token=target_dict.unk_word				,pad_token=target_dict.pad_word				,bos_token=target_dict.bos_word				,eos_token=target_dict.eos_word				,word_delimiter_token='''|'''				,do_lower_case=UpperCamelCase_				,)
               snake_case											=							WavaVecaProcessor(feature_extractor=UpperCamelCase_				,tokenizer=UpperCamelCase_							)
               processor.save_pretrained(UpperCamelCase_							)

          snake_case											=							SEWForCTC(UpperCamelCase_							)
     else:
          snake_case											=							SEWModel(UpperCamelCase_							)
          feature_extractor.save_pretrained(UpperCamelCase_							)

     recursively_load_weights(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							)

     hf_model.save_pretrained(UpperCamelCase_							)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Any      =       argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 _SCREAMING_SNAKE_CASE     :					str      =       parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()

         snake_case											=							[
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''的''',
             '''价''',
             '''格''',
             '''是''',
             '''15''',
             '''便''',
             '''alex''',
             '''##andra''',
             '''，''',
             '''。''',
             '''-''',
             '''t''',
             '''shirt''',
         ]
         snake_case											=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES['''vocab_file''']       )
         with open(self.vocab_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

         snake_case											=							{
             '''do_resize''': True,
             '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
             '''do_center_crop''': True,
             '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
             '''do_normalize''': True,
             '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
             '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
             '''do_convert_rgb''': True,
         }
         snake_case											=							os.path.join(self.tmpdirname     ,   __snake_case       )
         with open(self.image_processor_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as fp:
              json.dump(__snake_case     ,   __snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizer.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizerFast.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							[np.random.randint(2_5_5     ,   size=(3, 3_0, 4_0_0)     ,   dtype=np.uinta       )]

         snake_case											=							[Image.fromarray(np.moveaxis(__snake_case     ,   0     ,   -1       )       ) for x in image_inputs]

         return image_inputs
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_rust_tokenizer()
         snake_case											=							self.get_image_processor()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_slow.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,   use_fast=__snake_case       )

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_fast.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor_slow.tokenizer.get_vocab()     ,   tokenizer_slow.get_vocab()       )
         self.assertEqual(processor_fast.tokenizer.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertEqual(tokenizer_slow.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertIsInstance(processor_slow.tokenizer     ,   __snake_case       )
         self.assertIsInstance(processor_fast.tokenizer     ,   __snake_case       )

         self.assertEqual(processor_slow.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertEqual(processor_fast.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertIsInstance(processor_slow.image_processor     ,   __snake_case       )
         self.assertIsInstance(processor_fast.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''       )
         snake_case											=							self.get_image_processor(do_normalize=__snake_case       )

         snake_case											=							ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname     ,   cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''     ,   do_normalize=__snake_case       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.image_processor.to_json_string()     ,   image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							self.prepare_image_inputs()

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   processor.model_input_names       )




